Enterprise AI Architecture
Reference architectures for agents, RAG, and MLOps that hold up under real load, security review, and audit.
An architecture that survives a demo and an architecture that survives a security review are different documents. The first shows a happy path. The second has to answer what happens when retrieval returns the wrong tenant's document, when a model endpoint is called with a crafted prompt, and when the vendor changes a model behind a version you did not pin.
The gap usually shows up late, at the point where a working prototype meets an architecture review board, and it is expensive precisely because the prototype was successful. Rework at that stage costs more than designing for it did.
This engagement produces the architecture that passes the second review: component design, data flow, identity model, failure modes, and the infrastructure code to stand it up.
What the engagement includes
Reference architecture
Component and data-flow design for your agent, RAG, or MLOps workload — with trust boundaries drawn explicitly rather than implied.
Identity and authorisation model
Per-agent identity, scoped credentials, and per-action authorisation. Workload identity federation and SPIRE where the estate supports it.
Failure-mode analysis
What breaks, how it is detected, and what it degrades to. Including the failure modes specific to AI systems: retrieval leakage across tenants, prompt injection, model drift, and silent quality regression.
Terraform-first infrastructure
The architecture expressed as infrastructure code across AWS, Azure, or GCP — including GovCloud and Azure Government — so environments are reproducible rather than hand-built.
Review pack
The document set an architecture review board and a security reviewer need, prepared before the meeting rather than in response to it.
Who this is for
- Teams whose prototype must now pass architecture and security review
- Multi-cloud estates that need one coherent AI platform pattern
- Regulated workloads where the audit question arrives before launch
When this is the wrong engagement: If the question is which use cases to pursue rather than how to build them, start with enterprise AI strategy.
How it runs
- Discovery (30 minutes). A working call to map your stack, constraints, and the highest-value first step. No pitch deck.
- Scoped fixed-price proposal. A written statement of work with deliverables, milestones, and a fixed price — not an open-ended time-and-materials meter.
- Delivery. Senior architects execute against milestones, with governance applied to every AI-agent action from day one.
- Operate or hand off. Monitored operation, or a clean handoff with runbooks so your team can run it without us.
Questions we get asked
Do you work with our existing cloud provider and tooling?
Yes. The architecture is designed for the estate you have, across AWS, Azure, and GCP, including GovCloud and Azure Government. We are not incentivised toward a particular provider and do not resell their licences. Where your existing tooling is adequate, the recommendation is to keep it — replacing a working component is a cost, not a deliverable.
Is this a document or working infrastructure?
Both, and the split is set during scoping. The architecture is always delivered as written design plus the review pack. Terraform modules that stand the environment up are included where the engagement covers implementation. Some clients want the design so their own platform team can build it; that is a legitimate and common shape.
How do you handle prompt injection and retrieval leakage?
They are treated as architectural concerns, not prompt-engineering ones. Retrieval isolation is enforced at the data layer so a crafted prompt cannot reach another tenant's corpus, and agent actions are authorised per action against policy rather than trusted because the model asked. Prompt-level mitigations are a defence in depth on top of that, never the primary control.
What if we already have an architecture and want it reviewed?
That is a smaller and often more useful engagement. We review the design against production load, security review, and audit expectations, and return findings ranked by severity with specific remediations. If the architecture is sound, the review says so — a review that always finds catastrophic problems is selling the follow-on work.
Related services
Multi-Agent Systems
Fleets of governed agents coordinated around real workflows — not a demo that stalls after the meeting.
RAG Platform Development
Grounded retrieval on your own corpora, evaluated for accuracy and built to keep your data isolated.
AI Security Consulting
Threat modeling for LLMs, data pipelines, and model endpoints — plus the controls to close the gaps.
Start with a 30-minute discovery call
No pitch deck. We map your constraints and tell you the highest-value first step — including when that step is not an engagement with us.
Book a discovery call